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Vladimir Vapnik: Statistical Learning | Lex Fridman Podcast #5

Vladimir Vapnik is the co-inventor of support vector machines, support vector clustering, VC theory, and many foundational ideas in statistical learning.

Lex FridmanhostVladimir Vapnikguest

Nov 16, 201854mWatch on YouTube ↗

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EPISODE INFO

Released: November 16, 2018
Duration: 54m
Channel: Lex Fridman Podcast
Watch on YouTube: ▶ Open ↗

EPISODE DESCRIPTION

Vladimir Vapnik is the co-inventor of support vector machines, support vector clustering, VC theory, and many foundational ideas in statistical learning. He was born in the Soviet Union, worked at the Institute of Control Sciences in Moscow, then in the US, worked at AT&T, NEC Labs, Facebook AI Research, and now is a professor at Columbia University. His work has been cited over 200,000 times. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep5-sb See below for timestamps, and to give feedback, submit questions, contact Lex, etc. *CONTACT LEX:* *Feedback* - give feedback to Lex: https://lexfridman.com/survey *AMA* - submit questions, videos or call-in: https://lexfridman.com/ama *Hiring* - join our team: https://lexfridman.com/hiring *Other* - other ways to get in touch: https://lexfridman.com/contact *PODCAST LINKS:*
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SPEAKERS

Lex Fridman
host
Vladimir Vapnik
guest
Narrator
other

EPISODE SUMMARY

In this episode of Lex Fridman Podcast, featuring Lex Fridman and Vladimir Vapnik, Vladimir Vapnik: Statistical Learning | Lex Fridman Podcast #5 explores Vladimir Vapnik on learning, intelligence, and the limits of deep learning Vladimir Vapnik discusses the philosophical and mathematical foundations of statistical learning, contrasting instrumentalism (prediction) with realism (understanding "God's laws"). He argues that modern machine learning overemphasizes brute-force prediction and deep learning, while neglecting conditional probabilities, invariants, and the role of a "teacher" in providing powerful predicates. Vapnik introduces his view that there are two mechanisms of learning—strong and weak convergence—with weak convergence relying on high‑level predicates like “swims like a duck” that dramatically reduce data requirements. He sees the central open problem as understanding intelligence: how good teachers generate such predicates, and how to formalize that process to achieve learning with far fewer examples.

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